Real-time DWT-based compression for wearable Electrocardiogram monitoring system

Al-Busaidi, Asiya M.
Khriji, Lazhar
Touati, Farid
Rasid, Mohd Fadlee A.
Mnaouer, Adel Ben
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Institute of Electrical and Electronics Engineers Inc.
Compression of Electrocardiogram signal is important for digital Holters recording, signal archiving, transmission over communication channels and Telemedicine. This paper introduces an effective real-time compression scheme to overcome the limitation of payload size of the transmission channel. This scheme utilizes the Discrete Wavelet Transform (DWT), Bit-Field Preserving (BFP) and Running Length Encoding (RLE) methods which showed efficient compression results. The scheme dynamically checks if the compressed packets fit into the available payload. If not, the original signal will be divided into blocks and each block will be re-compressed again. The dynamic scheme was tested on large and small number of samples. The results show that a small block of 64, 128 or 256 samples will not affect the compression performance and no distortion occurred on the reconstructed signal. © 2015 IEEE.
This conference paper is not available at CUD collection. The version of scholarly record of this conference paper is published in 2015 IEEE 8th GCC Conference & Exhibition (2015), available online at:
Discrete wavelet transforms, Electrocardiography, Encoding (symbols), Signal processing, Telemedicine, Wavelet transforms, Wearable technology, Bit-field preserving, Compression performance, ECG compression, Electrocardiogram signalLength encoding, Length encoding, Number of samples, Real-time compressio, Transmission channels, Data compression
Al-Busaidi, A. M., Khriji, L., Touati, F., Rasid, M. F. A., & Ben Mnaouer, A. (2015). Real-time DWT-based compression for wearable Electrocardiogram monitoring system. In 2015 IEEE 8th GCC Conference and Exhibition, GCCCE 2015.